1. Field of the Invention
Apparatuses and methods consistent with the present invention relate to removing false contours, and more particularly, to removing false contours by smoothing a flat region with a false contour using shuffling of pixel values.
2. Description of the Related Art
As display devices increase in size in the digital television (DTV) era, artifacts become a huge problem. That is, when images are displayed on a cathode ray tube (CRT) monitor and small television, artifacts are not a problem. However, a large TV screen causes artifact problems. An image input with digital data is processed with contrast enhancement (CE) and detail enhancement (DE) so that an image with more definition can be obtained; however, the side effect of artifacts is generated.
Artifacts are unnatural elements in an image. False contour represents one of the artifacts. False contour is an artifact of the form of contour shown in flat region of an image such as the sky, the surface of the water, or the skin of a human being. All of the pixels of the flat region do not have the same value. Instead, the pixels of the flat region gradually change in brightness value. A differentiation in brightness value in the flat region is perceived by the human eye as a contour line. The contour line in the flat region is called a false contour or false edge as distinct from an edge of a signal element of an image.
There are a variety of causes for generating a false contour. However, when the quantization level generally indicating brightness value is not enough, false contour is generated. The quantization level deciding brightness value depends on a bit depth expressing digitalized brightness value. A false contour is not shown in the existing bit depth but shown in large display device. A false contour is also generated when an image is processed with CE and DE, compressed and restored in accordance with Joint Photographic Experts Group (JPEG) and Moving Picture Experts Group (MPEG) standards. To remove a false contour, the existing methods such as blue noise mask, dithering, and Daly and Feng has been used.
FIG. 1A to FIG. 1C are views showing the existing apparatuses for removing false contour. FIG. 1A is a view describing the existing method of Daly and Feng which removes false contour. The Daly and Feng method models the environment where false contour is generated and performs low pass filtering for the region of false contour that a human eye can recognize well so that false contour is removed. Here, bit depth of input image is P, and bit depth of input image passed through a low pass filter (LPF) 11 is R.
Referring to FIG. 1A, an input image with bit depth of P is input to LPF 11 according to the existing method. The LPF 11 adds peripheral pixels to pixels of input image so that the image smoothes. A bit depth of the input image passed through the LPF 11 increases by the added peripheral pixels. Accordingly, R which is a bit depth of the input image passed through the LPF 11 is more than P which is a bit depth of the input image.
A quantization part 12 re-quantizes a pixel value of the input image which is passed through the LPF 11 and added in bit depth.
A first adding part 13 outputs a difference value between an output value of the LPF 11 and an output value of the quantization part 12.
A second adding part 14 outputs a difference value between an output value of the first adding part 13 and a pixel value of the original image. That is, the second adding part 14 adds a difference between the re-quantized pixel value and the original pixel value to the original image. Therefore, a brightness value of the input image gradually changes so that false contour disappears.
However, the conventional method of removing false contour of Daly and Feng is applied to all of the pixels of the input image. Accordingly, as the entire input image passes through the LPF 11, an edge or texture corresponding to the signal element is blurred so that a degraded output image is obtained.
In addition, the Daly and Feng method can only be applied in a case where a bit depth of input image is less than a bit depth of output image, and where the original image before generating false contour by quantization is known. In addition, when a difference value between a value passed through the LPF 11 and re-quantized value is not enough to remove false contour, false contour can not be removed effectively.
FIG. 1B is a view describing a method for removing false contour generated in quantization process by lowering a bit depth for a certain digital image.
Referring to FIG. 1B, a double bit change detector 21 detects a change of pixel value corresponding to double of halftone information of the quantized digital image and a flat region detector 22 detects a change of peripheral pixels where double bits are detected. A random number generator 23 generates a certain value.
A signal corrector 24 receives a result of the double bit change detector 21, the flat region detector 22 and random number generator 23, and when a double bit change is detected and a peripheral pixel is flat region, the region where double bit change is detected is determined with false contour region. In addition, the signal corrector 24 removes the false contour by adding or subtracting random noise generated in the random number generator 23 to/from the pixel value of false contour.
However, this method can only be applied to a method for removing a false contour generated during a quantization process lowering bit depth so that the environment where quantization artifacts are generated needs to be exactly modeled. Additionally, this method is used for image quality degradation protection generated by a process for data compression by lowering bit depth of digital image so that the applicable field is limited.
FIG. 1C is a view describing a method for removing false contour generated by gamma correction in a plasma display panel (PDP) display. This method selectively performs dithering upon correcting gamma in a PDP display or expressing moving picture in a subfield method so that a false contour shown on flat region and around moving object is prevented.
Referring to FIG. 1C, a gamma and gain adjuster 31 corrects gamma of the input image and an error diffusion and dithering processor 32 dithers using a quantization error caused by correcting gamma. A false contour region detector 33 forecasts a false contour region to be occurred by a sub-field method and a selective dithering processor 34 selectively dithers the false contour region.
However, this method removes only a false contour which has occurred by gamma correction on the plasma display panel (PDP) display and is limitedly applied to remove false contour.
Therefore, the above conventional methods of removing false contour can be applied only when the reason why false contour is occurred is known. It is difficult to find the exact false contour, and the false contour can not be effectively removed.